GithubHelp home page GithubHelp logo

jrash / duke-nlp-ws-2020 Goto Github PK

View Code? Open in Web Editor NEW

This project forked from duke-mlss/duke-nlp-ws-2020

0.0 0.0 0.0 1.87 MB

Duke Natural Language Processing Winter School 2020

Jupyter Notebook 100.00%

duke-nlp-ws-2020's Introduction

Duke Natural Language Processing Winter School 2020

Welcome to the Duke Natural Language Processing Winter School 2020! This repository will contain the lecture materials and assignments for the hands-on PyTorch sessions.

While there is no hard requirement to attend these sessions or complete the exercises, we do strongly recommend them! Many of the machine learning concepts being covered thoughout the course are best learned and reinforced by implementing the ideas in code yourself. Please come with a laptop ready to code!

Before you arrive

Required

Please have Python 3+ and PyTorch installed. We will be doing a lot of our development in IPython notebooks, so you'll likely want to have Jupyter installed as well, or have access to Colab. If you don't already have the aforementioned software installed, please go through the notebook labeled 0A_PyTorch_Installation.ipynb. Installing these should take about 5-10 minutes.

Optional

Given the pace of the course, we'll be assuming some background knowledge for scientific computing in Python. If you are unfamiliar with IPython notebooks or Python coding environments, a brief introduction can be found in 0B_Coding_Environments.ipynb.

If you haven't used Python before, or want a refresher, we recommend Python Like You Mean It, by Ryan Soklaski. This free e-book consists of five short modules introducing Python for scientific computing and data analysis. Modules 1 and 2, on installing Python and Python essentials, will be especially useful. Module 3, which concerns the manipulation of matrices and vectors in Python, is very relevant but optional reading, as we will also be covering those topics in our sessions. Alternatively, we provide a quick crash course in 0C_Python_Prerequisites.ipynb.

The easiest way to download these materials is to click the green "Clone or download" button near the top of this GitHub repo, and then Download ZIP. However, we may update the materials in this GitHub repository as the course goes on. If you're familiar with Git, the most seamless way to keep your files up-to-date is by cloning/forking this repository and pulling. Alternatively, you can re-download this repo periodically, but you'll end up with duplicates.

duke-nlp-ws-2020's People

Contributors

laraqianyang avatar kevinjliang avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.